Episode Description

Summary

Most companies are sitting on customer data that could reshape their product strategy—they just can't read between the lines to find it. In this episode of the Growth Wizards Podcast, host Geoffrey Lugli sits down with Claudia Natasia, co-founder and CEO of Riley, for a sharp conversation about turning messy customer data into revenue intelligence. Claudia started her career as a data scientist, where she built an embeddings model that analyzed thousands of customer support calls and uncovered a hidden insight: customers thought a loyalty program was actually a payments product. That single finding drove nearly $50 million in revenue growth in under a year. It also planted the seed for Riley, which she describes as a GPS for revenue—an always-on intelligence engine that tells revenue teams which accounts to prioritize, where churn risk lives, and what moves will actually move the needle. 

Claudia and Jeff get into how Riley differentiates in a crowded AI market by building proprietary models that understand each customer's internal nomenclature from day one, why she refuses to do seat-based pricing for a data product, how 80% of their inbound unexpectedly came from rev ops leaders instead of product teams, and what she's learned about finding companies that are ready to buy now versus those still experimenting. If you lead revenue, run a GTM team, or are building an AI-powered product, Claudia's perspective on solving revenue problems instead of AI problems is worth your time.

Timestamps

• [00:27] – Claudia's journey from data scientist to CEO
• [02:17] – The $50 million insight hiding in customer support calls
• [04:35] – Riley's biggest GTM challenge: finding companies ready for intelligence, not just Q&A
• [06:20] – How proprietary models and custom ontology cut through AI noise
• [08:40] – The GPS metaphor: why we have it for driving but not for business
• [12:52] – Why seat-based pricing contradicts data democratization
• [15:06] – The ICP pivot: how rev ops became Riley's core buyer
• [17:23] – Finding your voice to stand out when every channel keeps changing

Takeaways

• Listen beyond what customers say explicitly—build models that read between the lines of support calls and product usage
• Differentiate your AI product by solving a business outcome (reduce churn, grow ACV) rather than selling the technology itself
• If you want to democratize data access, don't gate it behind seat-based pricing
• Let your ICP find you through inbound signals, then follow that data even when it contradicts your original thesis
• Use a strong, opinionated voice backed by data to cut through crowded channels—every time you come back to it, it wins
• Anchor technical products in metaphors people already understand (GPS for revenue) to make them instantly accessible

Guest LinkedIn: https://www.linkedin.com/in/claudianatasia/

Company website: https://www.askriley.io

Summary

Handing every employee an LLM license isn't an AI strategy — it's barely scratching the surface. In this episode of the Growth Wizards podcast, host Jeff Lugli sits down with Randhir Vieira, Managing Director at Petra Coaching and a 20+ year product leader with stops at Yahoo, Headspace, Omada Health, and HealthPy. Randhir shares what he's learned coaching digital health leaders through one of the fastest-moving technology shifts in a generation, and why the companies winning with AI are the ones pairing tools with governance, purpose, and a deep understanding of the user. He opens up about his own journey from pre-diabetic to losing 15% of his body weight through lifestyle change, and how that personal experience shapes his belief that meaningful transformation — whether in health or in business — happens in small, deliberate steps. The conversation also dives into what makes high-performing teams tick (from Saturday Night Live to emergency rooms), why psychological safety is a business-critical issue, and the single most impactful exercise a new manager can run with their team. Whether you're leading a health tech startup, navigating an AI rollout, or stepping into your first management role, Randhir's practical wisdom will leave you with something to try tomorrow.

Timestamps

[00:30] – Randhir's scenic path through five startups and three health tech companies [02:45] – Assessing where your company really stands on AI maturity [04:46] – Small AI habits that compound: building your own agent [08:16] – Why health tech can no longer stay in its silo [10:20] – Designing AI experiences that stand out from the noise [12:47] – Building feedback-friendly teams and the cost of silence [15:37] – The team norms exercise every new manager should run [18:49] – Parting advice: just get your hands dirty

Takeaways

• Tie every AI initiative back to a clear business metric, not just adoption numbers
• Identify your most annoying recurring task and build an AI agent to handle it
• Pair AI with human care rather than treating it as an all-or-nothing choice
• Run a 60–90 minute team norms session to make implicit expectations explicit
• Speak up earlier in your career — your perspective is more valuable than you think
• Pick one small, meaningful problem and start experimenting with AI today

You think you're behind on AI adoption, here's a visual that proves otherwise:

https://x.com/NoahEpstein_/status/2025605338779496797/photo/1

AI companies are pulling ahead, and here is why: https://tomtunguz.com/communication-tax-small-orgs/

To learn more about Randhir and his work, visit www.petracoaching.com

Connect

Guest LinkedIn: https://www.linkedin.com/in/randhirv 

Company website: https://petracoach.com

Leading the Merge: CRO Greg Davis on Customer-First GTM, AI Signals, and the .brand Opportunity

Summary

Selling a complex offering while integrating two companies and riding the AI wave? 

Greg Davis, Chief Revenue Officer at Mark Monitor Group, breaks down how to simplify complexity, scale trust, and drive growth. Greg shares how the Mark Monitor–Com Laude merger came together around a “customer-first, white-glove” ethos—structuring leadership early, building SME overlays, and aligning sales with operations for seamless handoffs. 

He details how AI now powers their go-to-market: a 28-language website chatbot, regionalized content, intent signals for targeted outreach, and strict data guardrails. 

Greg also unpacks two repeatable plays—leaning into GlobalBlock in 2024 to protect brands (and double expected growth) and mobilizing for ICANN’s new .brand application window with specialists, a focused landing page, and fast expert routing. He closes with timeless lessons: lead with problems (not products), co-author ROI with customers, and keep sales, marketing, and RevOps speaking the same language.

Timestamps

[00:45] – From finance and trading to CRO: coaching, simplifying complexity, and commercial growth

[02:56] – Translating technical value to outcomes: multi-stakeholder selling and co-authoring ROI

[04:47] – Inside the Mark Monitor–Com Laude merger: culture fit, GTM integration, and customer-first guardrails

[08:38] – Building SME overlays and “white-glove” ops alignment to serve global accounts at scale

[10:09] – AI in GTM: 28-language chatbot, regionalized site, intent signals, and strict data protection

[14:58] – Customer-first campaigns that win: GlobalBlock as a protective play (and growth driver)

[17:49] – The .brand window: why ICANN’s new round matters for trust, security, and control

[20:57] – What he’d learned sooner + parting advice: problem-first selling and GTM alignment

Takeaways

- Lead with customer problems—then co-author success metrics and ROI with buyers.

- Operationalize “customer-first” in mergers: set leadership early, align systems, and design white-glove handoffs.

- Deploy multilingual chat + regional content to cut speed-to-lead and meet customers where they are.

- Use intent signals to personalize outreach; retire generic sequences.

- Add SME overlays across sales for deep, complex conversations without slowing deals.

- Lean into market shifts that protect customers (e.g., GlobalBlock) even if they seem to cannibalize short-term revenue—and prep now for the ICANN .brand application window.

Pricing AI Without the Guesswork: Outcome-Based Models, Credits, and CFO Predictability

Summary

If pricing AI is breaking your forecast, this episode shows you how to regain control. 

Ping Identity’s Product Management Director, Stefan Kontschinsky—formerly of PwC and Okta—breaks down how to price AI in a way CFOs can trust and customers value. 

He explains the critical difference between what you sell (per user, per usage, per outcome) and how customers buy (contracting and consumption), then shares concrete levers to restore predictability: real-time consumption dashboards, clear limits, and smart overage policies. 

Stefan makes the case for outcome-based pricing as a differentiator, with examples from Intercom, Fin.ai, and even SpaceX’s “cargo delivered” model. He walks through a practical 2x2 of pricing tactics (auto-upgrades, burst protection, committed capacity, pooled credits) and demystifies credits with a resource-unit approach he deployed at Okta. 

You’ll also learn his monetization flywheel—quantify, create, track, and communicate value—and how AI can unify siloed sales/CS data to inform packaging and prove ROI. We wrap with what’s next: more optionality (bundles, seats, credits), how to hedge against rising model costs, and why reducing vendor lock-in is now a pricing strategy.

Timestamps

[00:21] – Guest intro and monetization journey: PwC to Okta to Ping Identity

[02:34] – Pricing AI 101: what you sell (user/usage/outcome) vs. how you buy (contract/consumption)

[05:07] – CFO predictability: consumption dashboards, limits, and proactive overage policies

[06:34] – Stand out with outcomes: examples from Intercom/Fin.ai and SpaceX’s “cargo delivered”

[08:14] – Tactics that work: auto-upgrades, burst protection, committed capacity, pooled credits, resource units

[11:06] – The monetization flywheel: quantify, create, track, and communicate value across Sales and CS

[14:01] – AI for pricing/packaging: un-siloing data to shape bundles and prove value

[15:54] – What’s next: optionality, rising token costs, and reducing lock-in risk

Takeaways

- Separate what you sell from how customers buy to avoid defaulting to pure usage billing.

- Build CFO-friendly predictability: show consumption in real time, set limits, and define overage policies.

- Pilot outcome-based pricing where you can; it differentiates and aligns price with delivered value.

- Offer committed capacity and pooled credits; use auto-upgrades and burst protection to balance risk.

- Standardize value quantification and close the loop with CS to track baselines, outcomes, and ROI.

- Use AI to unify pricing, sales, and CS data so packaging decisions reflect real customer behavior and value.

From Partnerships to Agentic AI: Lumavac’s Robert Macdonald on Scaling Without Sprawl

Summary

If AI pilots are popping up across your org but nothing sticks, you may be building on cracked foundations, fueling “agent sprawl,” and trying to automate the past.

Robert Macdonald, co-founder of Lumavac and former leader at Google, Pinterest, and Fox (plus a stint as Chief Product Officer in EdTech), shares how to make agentic AI real—and scalable. Drawing on 25 years in partnerships and product, Robert breaks down a practical path: start with one use case, fix technical debt, immerse cross-functional teams, and align on a shared playbook.

He unpacks Lumavac’s hybrid approach—Velocity—a combination of tool and hands-on guidance that centers on spec-driven development, context engineering, and secure system agents. Robert also reveals his partnership blueprint, inspired by Stuart Diamond’s “Getting More”: build on authentic value exchanges, spot early red flags, and co-develop with product and engineering to move faster together.

Expect concrete advice for CISOs, execs, and GTM leaders on establishing ownership, reducing chaos, and turning curiosity into consistent outcomes.

Timestamps

[00:45] – Robert’s path: Google, Pinterest, Fox, EdTech CPO, and why he founded Lumavac

[03:22] – The partnership playbook: value exchange, transparency, and “Getting More”

[07:41] – Early red flags: guarded partners, low investment, and resetting with better questions

[13:24] – Top AI mistakes: cracked foundations, agent sprawl, and automating the past

[16:12] – Immersion over memos: getting execs and non-technical teams hands-on

[18:55] – Where to start: single use cases, ownership, and calming CISO concerns

[20:54] – Inside Velocity: spec-driven development, context engineering, and system agents

[27:39] – Differentiation and GTM: hybrid tool + services, co-building, and “start with one”

Takeaways

- Build partnerships on explicit value exchange and transparent goals; use “Getting More” to co-create outcomes you couldn’t reach alone.

- Heed early red flags—rigidity, reluctance to share, and low time/resource commitment—and pause instead of pushing.

- Fix technical debt before scaling AI; don’t build on cracked foundations.

- Curb agent sprawl with standards, clear ownership, and a shared playbook.

- Reimagine workflows, don’t just automate the past—pursue productivity and efficiency together.

- Accelerate adoption through immersion: get cross-functional teams hands-on via spec-driven development, context engineering, and secure system agents; start with one use case, then scale.

Humanizing B2B in the AI Age: Chris Banks on Thought Leadership, Brand Voice, and Event-Led Growth

Summary

Drowning in AI-generated sameness? Learn how to make your brand feel unmistakably human. Marketing leader Chris Banks—whose career spans hospitality, event tech, and B2B SaaS—shares practical ways to balance AI with authentic voices that convert. He explains why real people must front your content, how to source thought leaders across the org (not just execs), and the simple changes that make webinars, videos, and social posts feel personal. Chris details how to keep brand voice consistent when scaling with AI by giving it a strong “foundation” (tone, examples, and context), plus a roadmap to avoid research rabbit holes. He also lays out a playbook for AI adoption inside companies: appoint an internal advocate, show use cases with data, and apply the five whys to drive behavior change. Expect candid lessons on what AI still can’t do well (like believable ad voiceovers), smart workflow wins (briefs, edits, and organization), and how event-led motions—from live Q&A to playful mixology segments—shorten the buyer journey. Listeners will walk away with frameworks to humanize content, pick high-impact AI projects, and ship work that stands out.

Timestamps

[00:45] – Chris’s background and focus: humanizing B2B brands and driving lead gen

[01:22] – Countering AI noise: elevating internal thought leaders and putting faces to content

[04:56] – Brand voice at scale: give AI a foundation (tone, examples, context) and iterate

[06:44] – Avoiding the AI rabbit hole: roadmaps, credible sources, and “good enough” thresholds

[08:39] – High-impact AI adoption: nominate an internal advocate and apply the five whys

[11:17] – Workflow wins: faster briefs, AI-assisted video edits, and organizing for speed

[12:40] – What not to automate: sourcing pitfalls and why AI video ads still feel off

[14:52] – Standing out: diverse formats, live Q&A, and event tactics that accelerate pipeline

Takeaways

- Put real people front and center—identify thought leaders across roles and pair ideas with faces.

- Use AI as a support layer: feed it brand foundations (tone, examples, context) and then edit for nuance.

- Prevent time-sinks with a clear roadmap—define “good enough,” cite sources, and resist endless tweaks.

- Appoint an internal AI advocate to drive adoption; show use cases, data, and apply the five whys.

- Diversify formats (how-tos, live demos, explainers) and make webinars interactive with Q&A and gamified elements.

- Pilot AI where minutes compound (briefs, edits, organization) and skip what hurts authenticity (generic voices, lookalike scripts).

Cutting Through Sameness: AI SEO, Cross-Platform Authority, and PLG with Anders Ohl

Summary

If digital marketing is starting to feel the same everywhere, how do you actually stand out?

Anders Ohl, Chief Marketing Officer at ClickPoint Software (a lead management and sales automation platform), shares how he’s reframed SEO and go-to-market around cross-platform authority, great writing, and product-led growth.

With a background in film and fine art, Anders brings a brand-first, psychology-informed lens to AI and marketing. He explains why the era of keyword-stuffed content is over, how EEAT and semantic depth now win, and when to use AI as an agent versus a chatbot to truly simplify user workflows.

Anders details how shifting to targeted SEO and PLG produced near-100% qualified inbound, how he built an AI authority moat that outranked industry giants for “position zero,” and why compliance-forward marketing creates higher-value leads as regulations evolve.

He also breaks down the looming “post-software” challenge (vibe-coded agents), the comeback of direct mail in a digital stack, and how customer success—not sales—became ClickPoint’s growth engine.

Timestamps

[00:45] – Anders’ path from film and fine art to CMO at ClickPoint

[01:14] – AI’s impact on marketing: from “SEO tactics” to cross-platform visibility and brand authority

[03:13] – The end of bad SEO writing: EEAT, semantic depth, and real thought leadership

[04:00] – Using AI as a cognitive sparring partner; pushing back to get better outcomes

[05:42] – Agents vs. chatbots: designing AI to simplify, not add friction

[07:32] – Strategy in action: moving to targeted SEO + product-led growth for qualified demand

[08:52] – Building an AI authority map: winning position zero and outranking bigger budgets

[11:20] – The “post-software” threat: vibe coding, AI agents, and productizing institutional knowledge

[13:43] – Beating sameness: brand fundamentals, hybrid analog/digital channels, and modern direct mail

[16:06] – Regulation shifts: why compliance-first marketing yields higher-converting leads

[16:50] – Replacing sales with customer success: free tier, support, and upgrade-by-outcome

[18:11] – Art, psychology, and business empathy as brand differentiators

[19:51] – Parting advice: stay alert—chaos = opportunity

Takeaways

- Write for authority, not algorithms—prioritize EEAT, semantic depth, and real thought leadership.

- Build cross-platform visibility: develop an “authority map” that lifts all related topics and pages.

- Treat AI as a collaborator: challenge outputs, bounce tools against each other, and demand specificity.

- Choose the right AI UX: use agents for one-click tasks and chat only where conversation adds value.

- Shift to product-led growth with customer success at the core; let outcomes drive upgrades.

- Differentiate your brand beyond best practices—blend digital with smart direct mail and stay compliant to earn higher-value leads.

Systematizing Nonprofit Growth: BGCMA’s SVP on Donor Pipelines, Story-Driven Events, and “Impact Subscriptions”

Summary

What happens when you bring a modern go-to-market playbook into a mission-driven nonprofit?

Shelly-Ann Wilson Anderson, Senior Vice President of Marketing & Communications at Boys & Girls Clubs of Metro Atlanta—and former CMO of Jamaica’s Exim Bank and PR/CSR leader at AT&T—shares how she’s building a predictable donor engine in a resource-constrained environment.

She breaks down a segmentation-led strategy across parents, youth influencers, donors, and alumni; the shift from one-off gifts to recurring “impact subscriptions”; and how automation can keep prospects warm year-round.

Shelly-Ann explains why in-person events “collapse the funnel,” detailing the narrative design behind BGCMA’s Youth of the Year Gala and how anchoring the story to Shaquille O’Neal unlocked national media and real-time social momentum.

She also offers a clear measurement model that pairs impact metrics with revenue KPIs, creative ways to stretch budgets with pro bono and matched media, and practical ways AI can multiply small teams without losing an authentic human voice.

Timestamps

[00:45] – Guest intro: From Jamaica’s financial sector to AT&T to leading marketing at BGCMA

[03:18] – Building a predictable donor engine: moving from relationships to a systematized GTM

[04:30] – Segmentation, digital campaigns, and converting to recurring “impact subscriptions”

[06:25] – Doing more with less: leveraging pro bono and matched media (e.g., metro digital billboards)

[07:51] – Measuring what matters: impact + revenue; KPIs for RD and program ops

[10:43] – In-person events as conversion accelerators: designing narratives that “collapse the funnel”

[13:18] – Case study: Youth of the Year Gala—Shaq’s authentic tie-in, national media, and real-time social

[18:39] – Storytelling and AI: multiplying content output while keeping the voice human

Takeaways

- Systematize donor acquisition with audience segmentation, multi-campaign digital programs, and recurring “impact subscriptions.”

- Design in-person events to convert: align theme, experience, and emotional storytelling with clear, on-the-spot CTAs.

- Pair impact and revenue: track ADA, teen engagement, new/recurring donors, event conversion, and post-event retention.

- Stretch limited budgets by tapping nonprofit advantages—pro bono placements, matched media, and partner networks.

- Use AI as a content multiplier to draft, refine, repurpose, and automate journeys—without sacrificing authenticity.

- Turn events into awareness flywheels: anchor stories to credible figures, publish in real time, and tie coverage to program outcomes.

From Seats to Usage: AI‑Era SaaS Pricing and Jobs-to-Be-Done Packaging with Roee Hartuv

Summary

AI is rewriting SaaS economics—and your pricing can no longer assume near-zero marginal costs.

Roee Hartuv, Senior Pricing Advisor and Head of GTM Expertise at willingness2pay.com, has helped 150+ SaaS and AI companies redesign pricing and packaging to unlock fast revenue growth.

He explains why pricing and packaging are the #1 growth levers, how AI’s token/API costs push teams from seat-based subscriptions to consumption models, and what to measure so revenue stays predictable.

Roee breaks down how to sell usage to enterprise CFOs, why hybrid models (base fee + metered usage) and caps restore confidence, and how real-time usage visibility prevents bill shock. He also shows how to differentiate with jobs-to-be-done packaging (not just good-better-best), creating cleaner initial sales and natural expansion paths.

Expect practical guidance, from the Salesforce “seat vs. usage” lesson to a simple 90-day pricing test and a reframing of GTM as one unified system.

Timestamps

[00:45] – Guest intro and why pricing/packaging is the top growth lever

[02:58] – AI’s impact on SaaS: from seat-based to usage and new unit economics

[05:36] – Metrics that matter in usage pricing and what Salesforce’s pivots reveal

[07:15] – Building buyer trust: real-time usage visibility and safe pricing “tests”

[08:48] – Fast revenue wins and migrating existing customers to new models

[10:20] – The enterprise CFO challenge: predictability in consumption pricing

[11:50] – Solutions: hybrid base + usage, caps, and usage forecasting

[14:53] – Differentiate with jobs-to-be-done packaging and clear expansion paths

Takeaways

- Instrument product to track granular usage events—not just logins—so you can meter fairly and bill confidently.

- Give buyers real-time usage/credit dashboards to prevent surprises and build trust.

- Offer hybrid pricing: a predictable base/platform or seat fee plus metered usage; add caps for enterprise predictability.

- Package by jobs-to-be-done instead of feature buckets to simplify buying and create natural expansion ladders.

- Run a 90-day pricing test: implement a 3–7% annual increase to keep pace with rising costs and validate price elasticity.

- Operate GTM as one system across marketing, sales, and customer success to reduce friction along the customer journey.

Systems, Not Slides: IBM’s Ray Bahari on AI-Driven Product Marketing and Launches That Learn

Summary

If your launches still run on “plan, ship, celebrate,” you’re leaving revenue on the table.

Ray Bahari, Global Product Marketing Lead at IBM and author of Systems, Not Slides: An AI Blueprint for Product Marketing, lays out how to move from launch theater to launch telemetry—compressing the distance between insight and execution.

Drawing on a career that spans scaling an early e‑commerce company, medtech at Stryker, mobile research at Polefish, insurance growth at Plymouth Rock, brand-building at JPMorgan’s Onyx, and ABM at IBM, Ray shares a practical playbook for adaptive positioning, sales enablement, and brand‑safe content at scale.

He explains how to build “minimum viable loops” across PMM—so positioning, proofs, messaging, and objections continuously inform each other—and how to choose a focused AI stack without drowning in tools.

Expect specifics on variant testing without losing brand consistency, creating a reusable bank of proofs, instrumenting journeys for speed-to-signal, and establishing a test‑and‑learn culture that actually sticks.

Timestamps

[00:45] – Guest intro and career arc: from early e‑commerce to SaaS, medtech, fintech, and IBM

[02:34] – Influencer dynamics in buying: lessons from Oticon and randomized rewards

[04:58] – Inside Stryker: digitizing the field with iPads and measurable customer interactions

[06:54] – Mobile-first research: Polefish and getting 2,000 survey responses overnight

[07:50] – Competing in insurance: partnerships, a homegrown DMP, and incrementality

[09:40] – IBM and JPMorgan: ABM impact, building Onyx, and returning to scale GTM with AI

[11:43] – From theater to telemetry: compression, adaptive positioning, and brand-safe variants

[17:16] – Tool choices, custom GPTs, and building Minimum Viable Loops and culture

Takeaways

- Build systems and loops—not one-off campaigns—to compress the time from insight to execution.

- Use AI to synthesize calls, CRM notes, and web behavior into objections, proofs, and enablement updates.

- Treat positioning as adaptive: test variants by segment and learn from “second-best” messages.

- Maintain brand consistency by training models on guidelines and anchoring content in a reusable bank of proofs.

- Curate a pragmatic AI stack: pick tools by task, leverage custom GPTs/LLMs, and document what works.

- Make test-and-learn cultural: start small, secure executive sponsorship, and run weekly operating rhythms.